


"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
 UpperCAmelCase				=						argparse.ArgumentParser(
     description=(
         """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
         """ Distillation"""
     )
 )
 parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
 parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
 parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
 parser.add_argument("""--vocab_transform""", action="""store_true""")
 UpperCAmelCase				=						parser.parse_args()

 if args.model_type == "roberta":
  UpperCAmelCase				=						RobertaForMaskedLM.from_pretrained(args.model_name)
  UpperCAmelCase				=						"""roberta"""
 elif args.model_type == "gpt2":
  UpperCAmelCase				=						GPTaLMHeadModel.from_pretrained(args.model_name)
  UpperCAmelCase				=						"""transformer"""

 UpperCAmelCase				=						model.state_dict()
 UpperCAmelCase				=						{}

 # Embeddings #
 if args.model_type == "gpt2":
  for param_name in ["wte.weight", "wpe.weight"]:
   UpperCAmelCase				=						state_dict[f'''{prefix}.{param_name}''']
 else:
  for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
   UpperCAmelCase				=						f'''{prefix}.embeddings.{w}.weight'''
   UpperCAmelCase				=						state_dict[param_name]
  for w in ["weight", "bias"]:
   UpperCAmelCase				=						f'''{prefix}.embeddings.LayerNorm.{w}'''
   UpperCAmelCase				=						state_dict[param_name]

    # Transformer Blocks #
 UpperCAmelCase				=						0
 for teacher_idx in [0, 2, 4, 7, 9, 11]:
  if args.model_type == "gpt2":
   for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
    for w in ["weight", "bias"]:
     UpperCAmelCase				=						state_dict[
         f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
     ]
   UpperCAmelCase				=						state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
  else:
   for layer in [
       "attention.self.query",
       "attention.self.key",
       "attention.self.value",
       "attention.output.dense",
       "attention.output.LayerNorm",
       "intermediate.dense",
       "output.dense",
       "output.LayerNorm",
   ]:
    for w in ["weight", "bias"]:
     UpperCAmelCase				=						state_dict[
         f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
     ]
  std_idx += 1

 # Language Modeling Head ###s
 if args.model_type == "roberta":
  for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
   UpperCAmelCase				=						state_dict[f'''{layer}''']
  if args.vocab_transform:
   for w in ["weight", "bias"]:
    UpperCAmelCase				=						state_dict[f'''lm_head.dense.{w}''']
    UpperCAmelCase				=						state_dict[f'''lm_head.layer_norm.{w}''']
 elif args.model_type == "gpt2":
  for w in ["weight", "bias"]:
   UpperCAmelCase				=						state_dict[f'''{prefix}.ln_f.{w}''']
  UpperCAmelCase				=						state_dict["""lm_head.weight"""]

 print(f'''N layers selected for distillation: {std_idx}''')
 print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

 print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
 torch.save(compressed_sd, args.dump_checkpoint)


from __future__ import annotations

from collections import Counter
from random import random




class 					_UpperCAmelCase			:



 '''simple docstring'''




 def __init__(		self				:   List[str])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   str)						->	None:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   str							, lowercase_				:   str							, lowercase_				:   float)						->	None:


   """simple docstring"""







   if nodea not in self.connections:
     self.add_node(lowercase_)
   if nodea not in self.connections:
     self.add_node(lowercase_)
   _UpperCamelCase           =    probability







 def 		__UpperCAmelCase				(		self				:   Any)						->	list[str]:


   """simple docstring"""







   return list(self.connections)







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   str)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    0
   _UpperCamelCase           =    random()

   for dest in self.connections[node]:
     current_probability += self.connections[node][dest]
     if current_probability > random_value:
       return dest
   return ""


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->dict[str, int]:




  '''simple docstring'''

  _UpperCamelCase           =    MarkovChainGraphUndirectedUnweighted()

  for nodea, nodea, probability in transitions:
    graph.add_transition_probability(a__    ,     a__    ,     a__ )

  _UpperCamelCase           =    Counter(graph.get_nodes() )
  _UpperCamelCase           =    start

  for _ in range(a__ ):
    _UpperCamelCase           =    graph.transition(a__ )
    visited[node] += 1

  return visited


if __name__ == "__main__":
   import doctest

   doctest.testmod()




